| Abstract: | Cluster validation is a major issue in cluster analysis of data mining, which is the process of evaluating performance of
clustering algorithms under varying input conditions. Many existing validity indices address clustering results of low-dimensional
data. Within high-dimensional data, many of the dimensions are irrelevant, and the clusters usually only exist in some projected
subspaces spanned by different combinations of dimensions. This paper presents a solution to the problem of cluster validation
for projective clustering. We propose two new measurements for the intracluster compactness and intercluster separation of
projected clusters. Based on these measurements and the conventional indices, three new cluster validity indices are presented.
Combined with a fuzzy projective clustering algorithm, the new indices are used to determine the number of projected clusters
in high-dimensional data. The suitability of our proposal has been demonstrated through an empirical study using synthetic
and real-world datasets. |
